Objectives

  1. End to end analysis using R
  2. Learn the caret package for ML
  3. Learn to present the case using R Notebooks

Read in the dataset

I stored the raw files on Github, so I used RCurl with Wehrley’s method that utilizes read.csv to the fullest. It’s one of the best ways I’ve found to read in data and also set data-types at the same time. He’s done a great job on that function. The dataset contains one ID variable, one response variable and ten predictor variables.

library(RCurl,quietly = T)
library(tidyverse,quietly = T)
library(ggplot2,quietly = T)
library(gridExtra,quietly = T)
library(Amelia,quietly = T)
library(beanplot,quietly = T)
library(caret,quietly = T)
library(stringr,quietly = T)
library(party, quietly = T)
# library(rattle, quietly = T)
readData <- function(path.name, file.name, column.types, missing.types) {
    gurl <- paste(path.name,file.name,sep="")
    download.file(gurl,file.name,method="curl",quiet = T)
    tbl_df(read.csv(file.name,colClasses=column.types,
             na.strings=missing.types))
}
Titanic.path <- "https://raw.githubusercontent.com/rsangole/Titanic/master/"
train.data.file <- "train.csv"
test.data.file <- "test.csv"
missing.types <- c("NA", "")
train.column.types <- c('integer',   # PassengerId
                        'factor',    # Survived
                        'factor',    # Pclass
                        'character', # Name
                        'factor',    # Sex
                        'numeric',   # Age
                        'integer',   # SibSp
                        'integer',   # Parch
                        'character', # Ticket
                        'numeric',   # Fare
                        'character', # Cabin
                        'factor'     # Embarked
)
test.column.types <- train.column.types[-2]     # # no Survived column in test.csv
train.raw <- readData(Titanic.path, train.data.file,train.column.types,missing.types)
test.raw <- readData(Titanic.path, test.data.file,test.column.types,missing.types)
prep_data <- function(D) {
    if (!is.null(D$Survived)) {
        D$Survived <- factor(D$Survived,
                             levels = c(1, 0),
                             labels = c('Survived', 'Dead'))
        }
    D$Pclass <- factor(D$Pclass,
                       levels = c(1, 2, 3),
                       labels = c('P1', 'P2', 'P3'))
    D$PassengerId <- NULL
    D
}
train.raw <- prep_data(train.raw)
test.raw <- prep_data(test.raw)
str(train.raw)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   891 obs. of  11 variables:
 $ Survived: Factor w/ 2 levels "Survived","Dead": 2 1 1 1 2 2 2 2 1 1 ...
 $ Pclass  : Factor w/ 3 levels "P1","P2","P3": 3 1 3 1 3 3 1 3 3 2 ...
 $ Name    : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
 $ Sex     : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
 $ Age     : num  22 38 26 35 35 NA 54 2 27 14 ...
 $ SibSp   : int  1 1 0 1 0 0 0 3 0 1 ...
 $ Parch   : int  0 0 0 0 0 0 0 1 2 0 ...
 $ Ticket  : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
 $ Fare    : num  7.25 71.28 7.92 53.1 8.05 ...
 $ Cabin   : chr  NA "C85" NA "C123" ...
 $ Embarked: Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...

Missing values analysis

Quick investigation of missing values can be done using the complete.cases(), and more thorough graphical summary can be done using Amelia. Overall, 79% of the observations have some missing data.

#Complete cases (percentages)
round(prop.table(table(complete.cases(train.raw))),2)

FALSE  TRUE 
 0.79  0.21 

Amelia lets us graphically investigate which variables have missing data. purr::map_xxx() gives this same information numerically in a succint fashion.

missmap(train.raw, main='Missing Values Analysis using Amelia ordered by % missing', col=c('red', 'gray'),legend = F,rank.order = T)

#Missing cases (numbers):
map_int(train.raw,~sum(is.na(.x)))
Survived   Pclass     Name      Sex      Age    SibSp    Parch   Ticket     Fare    Cabin Embarked 
       0        0        0        0      177        0        0        0        0      687        2 
#Missing cases (percentages):
round(map_dbl(train.raw,~sum(is.na(.x))/length(.x)),2)
Survived   Pclass     Name      Sex      Age    SibSp    Parch   Ticket     Fare    Cabin Embarked 
    0.00     0.00     0.00     0.00     0.20     0.00     0.00     0.00     0.00     0.77     0.00 

Cabin has a large number of missing values (77% missing). Imputing this variable may prove challenging or even useless. Age (19.9% missing) and Embarked (0.2%) missing are much more managable.


EDA

The first step in the analysis is to explore the data numerically and graphically. I always split up my EDA investigation as follows:

  • Target Variable
  • Predictor Variables
    • Univariate
    • Bivariate
    • Multivariate

This gives me a structured approach towards larger datasets. My professor at Northwestern taught me to always complete a thorough intimate numeric & graphical EDA on the data, no matter how large the data 1. Anscombe (1973) clearly shows the importance of graphical analyses.

Target Variable

Survived is the response variable. As we can see, a large majority of the passengers did not survive the accident. The response variable is a False/True boolean variable. Thus, the analysis techniques used later will be those appropriate for classification problems.

round(prop.table(table(train.raw$Survived)),2)

Survived     Dead 
    0.38     0.62 

Predictor Variables

Univariate & Bivariate

The first step is to look at every variable available. I prefer using the ggplot2 framework for all the visuals.

Continuous Variables

  • Age seems to have a bimodal distribution - very young children, and then directly young adults to mid-age persons. The 2nd mode is right skewed with no obvious outliers.

  • Fare certainly shows many outliers beyond the ~$200 level. A majority of the fares are <$50, which makes sense since a majority of the travelers are bound to be in the 3rd passenger class.

p1 <- ggplot(data=train.raw,aes(x=Age))+geom_histogram(bins = 40)
p2 <- ggplot(data=train.raw,aes(x=Fare))+geom_histogram(bins = 40)
grid.arrange(p1,p2)

As we can see, the median fare is $14.5, the mean is $32, but the max is $512. We’ll investigate winzorising this variable in the latter part. Perhaps a transformation will also help?

summary(train.raw$Fare)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    7.91   14.45   32.20   31.00  512.33 

Categorical Variables

A ggplot command is iterated over for the categorical variables.2

Key takeways for the categorical variables:

  1. Pclass: If you were traveling 1st class, you have the highest chance of survival. Could be indicative of preferential treatment to those who paid more, a less politically correct class-stratified society, as well as the fact that the 1st class passengers had cabins at the very top of the ship.
  2. Pclass: Persons traveling 3rd class had the highest fatality rate. 3rd class passengers had cabins deep in the ship. With the reasons give in (1), this could have contributed to the low survival rate.
  3. Sex: Males have a very high fatality rate. Seems like the ‘women and children’ first policy was followed during evacuation.
  4. SibSp & Parch: What’s interesting here is, for both these variables, at level 0, the fatality rate is higher. At levels 1+, the chances of survival are much better. Again, this could point to the ‘women and children’ policy being followed. (Or perhaps there weren’t as many families with children on board!)
  5. Embarked: Southampton has a higher fatality rate than Cherbourg or Queenstown. A cross-tabulation between Embarked and Pclass shows that 72% of the 3rd class passengers and 89% of the 2nd class passengers boarded at Southampton. This jives with the observation that 2nd and 3rd class passengers have higher fatality rates.
get_legend<-function(myggplot){
  tmp <- ggplot_gtable(ggplot_build(myggplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)
}
p <- lapply(X = c('Pclass','Sex','SibSp','Parch','Embarked'),
            FUN = function(x) ggplot(data = train.raw)+
                aes_string(x=x,fill='Survived')+
                geom_bar(position="dodge")+
                theme(legend.position="none"))
legend <- get_legend(ggplot(data = train.raw,aes(x=Pclass,fill=Survived))+geom_bar())
grid.arrange(p[[1]],p[[2]],p[[3]],p[[4]],p[[5]],legend,layout_matrix =
                 cbind(c(1,2,3),c(4,5,NA),c(6,6,6)),widths=c(3,3,1))

# round(prop.table(table(train.raw$Embarked,train.raw$Pclass),margin = 2),2)

Multivariate Analyses

Grouped boxplots are a common method of comparing distributions grouped by categorical variables. I find beanplots to be excellent complementary plots to boxplots (and in some cases, even better). They’re a bit tricky to read at first - since they are so underutilized - but just through one plot, a wealth of information can be extracted.3

Here is a comparison of the same information between a boxplot and a beanplot. What can we infer from the bean plot better?

  1. The beanplot allows us to visualize the density function of the parameter, in this case: Age. Furthermore, the length of each beanline is cumulative to the number of datapoints that exist. Rightaway, we can tell that Pclass=3 has the most data in the set, with sparser data at Pclass=1.
  2. The mean values for 1st class is higher than that for 2nd and 3rd class. The distributions of deceased and survived for 1st class are fairly similar.
  3. For 2nd and 3rd class, the survived data shows a bimodal distribution. Bumps at the 0-10 age show that children were evacuated first. This is also the reason the mean values for survived is lower.
  4. For 2nd and 3rd class, the deceased data shows a fairly normal distribution.
  5. The individual measurements (represented by black lines) represent each observation and help identify outliers much more easily than a boxplot does.
ggplot(train.raw,aes(y=Age,x=Pclass))+geom_boxplot(aes(fill=Survived))+theme_bw()

beanplot(Age~Survived*Pclass,side='b',train.raw,col=list('yellow','orange'),
         border = c('yellow2','darkorange'),ll = 0.05,boxwex = .5,
         main='Passenger survival by pclass and Age',xlab='Passenger Class',ylab='Age')
legend('topright', fill = c('yellow','orange'), legend = c("Dead", "Survived"),bty = 'n',cex = .8)

A look into the SibSp and Parch variables shows something interesting. There are three regions one can identify:

  • The probability of survival is minimal for number of parents/children aboard > 3.
  • The probability of survival is minimal for number of siblings/spouses aboard > 3.
  • For SibSp<=3 and Parch<=3, there are better chances for survival.

The grouping by Pclass reveals that all the large families were 3rd class travelers. Worse access to help… lowest chance for survival.

These could be simple rules either hard coded during model building: something along the lines of: IF (SibSp>3 OR Parch >3) THEN prediction = 0, or some derived variables can be created.

ggplot(train.raw,aes(y=SibSp,x=Parch))+
    geom_jitter(aes(color=Survived,shape=Pclass))+
    theme_bw()+
    scale_shape(solid=F)+
    geom_vline(xintercept = 3,color='darkblue',lty=3)+
    geom_hline(yintercept = 3,color='darkblue',lty=3)


Data Preparation

Missing Values Imputation

Starting with the easier one first:

Embarked: The largest portion of the passengers embared at Southhampton. I’m replacing the NAs with the same. First, I create a new imputed training dataset.

summary(train.raw$Embarked)
   C    Q    S NA's 
 168   77  644    2 
train.imp <- train.raw
train.imp$Embarked[is.na(train.imp$Embarked)] <- 'S'

Names, Titles & Age:

The names have titles embedded in the strings. I can extract these using regex. Master, Miss, Mr and Mrs are the most popular - no surprise there, with lots of other titles. Here’s the distribution of the titles by age. These can be used to impute the missing age values.

train.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = train.raw$Name)
train.raw$title <- as.factor(train.raw$title)
train.raw %>%
    na.omit() %>%
    group_by(title) %>%
    dplyr::summarise(Count=n(), Median_Age=round(median(Age),0)) %>%
    arrange(-Median_Age)
ggplot(train.raw,aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(shape=21,alpha=.6,col='blue')+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')

Grouping similar titles together, I’ve kept a few titles - Officer, Royalty, Mr, Mrs and Miss.

train.imp <- train.raw
train.imp$title <- as.character(train.imp$title)
train.imp$title[train.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
train.imp$title[train.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
train.imp$title[train.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
train.imp$title[train.imp$title %in% c('Ms','Mlle')] <- 'Miss'
train.imp$title <- as.factor(train.imp$title)
train.imp %>%
    group_by(title) %>%
    summarise(Median_Age=median(Age,na.rm = T))
ggplot(train.imp,aes(x=title,y=Age))+
    geom_jitter(color='blue',shape=21,alpha=.7)+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1))+
    labs(caption='Red points are median values')

Now for the missing Age values. I’m trying out two strategies to impute age, just for kicks. First, a regression tree using the rpart method. 5-repeat 10-fold cross validation across a tuning grid of 20 values of maxdepth. RMSE stablizes at a depth of 14, with a value of 12.2.

age.predictors <- train.imp %>%
    dplyr::select(-Survived,-Cabin,-Ticket,-Name) %>%
    filter(complete.cases(.))
set.seed(1234)
ctrl <- trainControl(method = "boot",
                     repeats = 5,
                     number = 200
                     )
rpartGrid <- data.frame(maxdepth = seq(4,20,2))
rpartFit <- train(Age~.,
                  data=age.predictors,
                  method='rpart2',
                  trControl = ctrl,
                  tuneGrid = rpartGrid
                  )
rpartFit
CART 

712 samples
  7 predictor

No pre-processing
Resampling: Bootstrapped (200 reps) 
Summary of sample sizes: 712, 712, 712, 712, 712, 712, ... 
Resampling results across tuning parameters:

  maxdepth  RMSE      Rsquared 
   4        12.91352  0.2172555
   6        12.56362  0.2600303
   8        12.37466  0.2835666
  10        12.28184  0.2961068
  12        12.23967  0.3028092
  14        12.23329  0.3046570
  16        12.24043  0.3041673
  18        12.23669  0.3045630
  20        12.23821  0.3044234

RMSE was used to select the optimal model using  the smallest value.
The final value used for the model was maxdepth = 14.
plot(rpartFit)

plot(rpartFit$finalModel,margin=0.02)
text(rpartFit$finalModel,cex=0.8)

Another way is to run a randomforest with a search over values of mtry using 5-repeat 10-fold cross validation. As we can see mtry=4 is the optimal value which results in the lowest RMSE of 11.4; much better than the rpart model.

set.seed(1234)
rfGrid <- data.frame(mtry=seq(1,6,1))
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5
                     )
rfFit <- train(Age~.,
                  data=age.predictors,
                  method='rf',
                  trControl = ctrl,
                  tuneGrid = rfGrid)
rfFit
Random Forest 

712 samples
  7 predictor

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 642, 640, 642, 641, 640, 639, ... 
Resampling results across tuning parameters:

  mtry  RMSE      Rsquared 
  1     12.46449  0.3816241
  2     11.33714  0.4192503
  3     11.05166  0.4263448
  4     11.04766  0.4217797
  5     11.10717  0.4152716
  6     11.20324  0.4066238

RMSE was used to select the optimal model using  the smallest value.
The final value used for the model was mtry = 4.
plot(rfFit)

I’m going to use the randomForest model. Using the predict.train() to predict values of age and plug them back into the imputed data. You can see the blue points which are the imputed values of Age. What I noticed is that for all the titles, the imputed Age value seems to be distributed fairly well, except Master. For Master, the three imputed are definitely outliers. I’m going to force these to the median Age.

missing.age <- train.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
train.imp %>%
    mutate(AgeMissing = is.na(Age),
           Age = ifelse(AgeMissing,age.predicted,Age)) %>%
    ggplot(aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(aes(y=Age,col=AgeMissing),shape=2)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')

train.imp$Age[is.na(train.imp$Age)] <- age.predicted
train.imp$Age[train.imp$title=='Master' & train.imp$Age > 20] <- median(train.imp$Age[train.imp$title=='Master'],na.rm = T)

Derived Variables

Child?: Trying out two engineered variables here - is the passenger a child or not? Using Age=18 as a threshold. And is s/he close enough to be considered a adult by chance? Those between 16 and 18 could be mistaken for not being children. (My way of incorporating a fudge factor in the decision process of ladies & children first.)

train.imp$child <- 0
train.imp$child[train.imp$Age<18] <- 1
train.imp$almostadult <- as.numeric(between(train.imp$Age,16,18))

Really young, or really old?: Really young ones and older folks would get priority perhaps. Creating two categorical binary variables for these conditions.

train.imp$Young <- ifelse(train.imp$Age<10,1,0)
train.imp$Seniors <- ifelse(train.imp$Age>60,1,0)

Family related: Let’s also create some variables that talk about family sizes. What’s the total family size – continous variable TotalFam. Is the person single, part of a couple or a large family? Three categorical variables for these.

train.imp$TotalFam <- train.imp$SibSp + train.imp$Parch + 1
train.imp$LargeParCh <- as.numeric(train.imp$Parch>=3)
train.imp$LargeSibSp <- as.numeric(train.imp$SibSp>=3)
train.imp$Single <- ifelse(train.imp$TotalFam==1,1,0)
train.imp$Couple <- ifelse(train.imp$TotalFam==2,1,0)
train.imp$Family <- ifelse(train.imp$TotalFam>4,1,0)
train.imp$Name <- NULL

Cabin related: Extracting the cabin alphabet and number from the cabin variable. Since the cabin numbers could be ordered from left to right or top to bottom on the boat, perhaps only the 1st digit is significant. Also, some folks have more than 1 cabin. Wonder if that’s important. Since lots of unknowns in the Cabin variable, all NA values are replaced by ‘U’. Refering to the deck diagram, the topmost decks are A and B, which are closest to the lifeboats. Perhaps that’s important too. Here, I create a bunch of categorical variables based off the original Cabin, and then remove it from the dataset.

train.imp$CabinMissing <- as.numeric(is.na(train.raw$Cabin))
train.imp$CabinCode <- map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
train.imp$CabinCode[is.na(train.imp$CabinCode)] <- 'U'
train.imp$CabinNum <- as.numeric(map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
train.imp$CabinNum <- map_int(train.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
train.imp$CabinNum[is.na(train.imp$CabinNum)] <- 0
train.imp$TopDeck <- ifelse(train.imp$CabinCode %in% c('A','B'),1,0)
train.imp$MidDeck <- ifelse(train.imp$CabinCode %in% c('C','D'),1,0)
train.imp$LowerDeck <- ifelse(train.imp$TopDeck==0 & train.imp$MidDeck ==0 ,1,0)
train.imp$NumberofCabins <- map_int(train.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
train.imp$Cabin <- NULL

Ticket: Lastly, the ticket variable. I’m not sure what to make of it, so I’m keeping it for now, after cleaning it up a bit. A majority (80%) of the rows have unique (one) ticket. 14% rows have a duplicate ticket, perhaps indicating a family. A small number of rows have 3+ duplicates of the tickets.

train.imp$Ticket %>% table() %>% as.numeric() %>% table()
.
  1   2   3   4   5   6   7 
547  94  21  11   2   3   3 

There seems to be a bit of a pattern here. Tickets starting with 1 are mostly 1st class, those starting with 2 are 2nd class, and 3 - 3rd class. But, I feel it’s a very loose association.

train.imp %>% group_by(Pclass) %>% dplyr::select(Ticket,Pclass) %>% sample_n(5)

What I’m going to do is clean up the columns (remove special characters, spaces etc), then split the Ticket column into four: TicketChar, TicketNum,TicketNumLength, TicketNumStart. (Upon running the script a few times, I’ve decided to get rid of TicketNum, but I’m commenting the code for future ref). The TicketChar variable as this distribution:

train.imp %<>%
    mutate(
        Ticket = str_to_upper(Ticket) %>%
            str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
        TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
        TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
        TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
        TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+')) 
        ) %>%
     mutate(
         TicketChar = map_chr(.x=TicketChar,
                              .f=~str_split(string=.x, pattern = '',simplify = T)[1])
         ) %>%     
    mutate(
        TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
        TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
        TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
    )
train.imp$Ticket <- NULL
train.imp$TicketNum <- NULL
table(train.imp$TicketChar,dnn ='TicketChar')
TicketChar
  A   C   F   L   P   S   U   W 
 29  47   7   4  65  65 661  13 
table(train.imp$TicketNumLen,dnn='TicketNumLen')
TicketNumLen
  1   3   4   5   6   7 
  6   7 165 246 423  44 
table(train.imp$TicketNumStart,dnn='TicketNumStart')
TicketNumStart
  0   1   2   3   4   5   6   7   8   9 
  6 231 230 365  15   9  14  15   3   3 

Winzoring Variables

The fare variable has one massive outlier. Winzorising this variable using the 95th percentile value as the cutoff.

# ggplot(train.imp,aes(x=Fare,fill=Pclass))+geom_histogram()+facet_grid(Pclass~.)
# quantile(train.imp$Fare[train.imp$Pclass=='P1'],probs = c(.1,.25,.5,.75,.95))
# train.imp$Fare[train.imp$Fare>232] <- 232

Final Data Review

The dataset is now prepared for modeling. Here’s a quick review of the data so far. 29 variables in total.

train.imp %>% glimpse()
Observations: 891
Variables: 29
$ Survived       <fctr> Dead, Survived, Survived, Survived, Dead, Dead, Dead, Dead, Survive...
$ Pclass         <fctr> P3, P1, P3, P1, P3, P3, P1, P3, P3, P2, P3, P1, P3, P3, P3, P2, P3,...
$ Sex            <fctr> male, female, female, female, male, male, male, male, female, femal...
$ Age            <dbl> 22.00000, 38.00000, 26.00000, 35.00000, 35.00000, 35.04936, 54.00000...
$ SibSp          <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1, 0, 0, 0, 0,...
$ Parch          <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Fare           <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625, 21.0750, ...
$ Embarked       <fctr> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q, S, S, C, S, S, Q...
$ title          <fctr> Mr, Mrs, Miss, Mrs, Mr, Mr, Mr, Master, Mrs, Mrs, Miss, Miss, Mr, M...
$ child          <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,...
$ almostadult    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ Young          <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Seniors        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ TotalFam       <dbl> 2, 2, 1, 2, 1, 1, 1, 5, 3, 2, 3, 1, 1, 7, 1, 1, 6, 1, 2, 1, 1, 1, 1,...
$ LargeParCh     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ LargeSibSp     <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Single         <dbl> 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,...
$ Couple         <dbl> 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ Family         <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ CabinMissing   <dbl> 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ CabinCode      <chr> "U", "C", "U", "C", "U", "U", "E", "U", "U", "U", "G", "C", "U", "U"...
$ CabinNum       <dbl> 0, 8, 0, 1, 0, 0, 4, 0, 0, 0, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0,...
$ TopDeck        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ MidDeck        <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ LowerDeck      <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ NumberofCabins <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ TicketNumStart <dbl> 2, 1, 3, 1, 3, 3, 1, 3, 3, 2, 9, 1, 2, 3, 3, 2, 3, 2, 3, 2, 2, 2, 3,...
$ TicketNumLen   <int> 5, 5, 7, 6, 6, 6, 5, 6, 6, 6, 4, 6, 4, 6, 6, 6, 6, 6, 6, 4, 6, 6, 6,...
$ TicketChar     <chr> "A", "P", "S", "U", "U", "U", "U", "U", "U", "U", "P", "U", "A", "U"...

Modeling

I’m experimenting with a few modeling techniques, mainly xgboost, gbm, and penalized models using glmnet. I’ve implemented all these models using caret which I find an absolutely indispensible toolkit to prep, build, tune and explore numerous models using very few lines of code.

For all models, I’m using a 5-repeat 10-fold cross validation technique on the training dataset. Thus, I have not split the training dataset further into test-train sets, given the small number of observations in the dataset.

Furthermore, given the 80:20 class-imbalance, I’m also trying out smote as an class balancing technique for a few models.

Tuning parameter searches (aka hypertuning) is performed using the tuneGrid parameter in the train() call. The best model is selected using the AUC of the ROC. Here are the models and a few intermediate results for each model. At the end, I’ve compared the performance of all the models together.

Extreme Gradient Boosting (xgboost - 5repeat-10fold-cv)

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary
                     # sampling = 'smote'
                     )
set.seed(1)
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
xgbFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
xgbFit
plot(xgbFit)
xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel) %>%
    xgb.ggplot.importance()

densityplot(xgbFit,pch='|')
predict(xgbFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)

Extreme Gradient Boosting (xgboost - 5test-train split)

ctrl <- trainControl(method = 'LGOCV',
                     number = 50,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary
                     # sampling = 'smote'
                     )
set.seed(1)
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
variable 'Survived' is not a factor
xgbFit.LGOCV <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Resample01: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample01: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample01: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample02: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample02: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample03: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample03: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample04: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample04: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample05: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample05: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample06: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample06: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample07: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample07: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample08: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample08: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample09: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample09: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample10: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample10: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample11: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample11: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample12: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample12: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample13: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample13: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample14: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample14: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample15: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample15: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample16: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample16: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample17: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample17: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample18: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample18: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample19: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample19: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample20: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample20: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample21: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample21: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample22: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample22: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample23: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample23: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample24: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample24: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample25: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample25: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample26: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample26: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample27: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample27: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample28: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample28: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample29: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample29: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample30: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample30: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample31: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample31: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample32: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample32: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample33: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample33: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample34: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample34: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample35: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample35: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample36: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample36: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample37: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample37: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample38: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample38: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample39: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample39: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample40: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample40: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample41: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample41: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample42: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample42: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample43: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample43: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample44: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample44: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample45: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample45: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample46: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample46: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample47: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample47: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample48: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample48: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample49: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample49: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Resample50: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Resample50: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
Aggregating results
Selecting tuning parameters
Fitting nrounds = 6, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbFit.LGOCV
eXtreme Gradient Boosting 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Repeated Train/Test Splits Estimated (50 reps, 75%) 
Summary of sample sizes: 669, 669, 669, 669, 669, 669, ... 
Resampling results across tuning parameters:

  eta  max_depth  nrounds  ROC        Sens       Spec     
  0.3  2          2        0.8520850  0.6623529  0.8795620
  0.3  2          3        0.8604663  0.6901176  0.8767883
  0.3  2          4        0.8644860  0.7225882  0.8690511
  0.3  2          5        0.8668854  0.7188235  0.8715328
  0.3  2          6        0.8675526  0.7312941  0.8683212
  0.3  2          7        0.8686638  0.7244706  0.8699270
  0.3  3          2        0.8649901  0.7284706  0.8654015
  0.3  3          3        0.8703864  0.7345882  0.8651095
  0.3  3          4        0.8726595  0.7287059  0.8740146
  0.3  3          5        0.8751009  0.7348235  0.8718248
  0.3  3          6        0.8749558  0.7336471  0.8728467
  0.3  3          7        0.8746337  0.7312941  0.8750365
  0.3  4          2        0.8669678  0.7367059  0.8589781
  0.3  4          3        0.8706509  0.7282353  0.8696350
  0.3  4          4        0.8729266  0.7261176  0.8750365
  0.3  4          5        0.8752452  0.7324706  0.8731387
  0.3  4          6        0.8750691  0.7256471  0.8751825
  0.3  4          7        0.8749180  0.7254118  0.8769343
  0.3  5          2        0.8643761  0.7204706  0.8648175
  0.3  5          3        0.8690245  0.7228235  0.8678832
  0.3  5          4        0.8725839  0.7209412  0.8728467
  0.3  5          5        0.8738695  0.7207059  0.8753285
  0.3  5          6        0.8740215  0.7261176  0.8750365
  0.3  5          7        0.8748759  0.7221176  0.8804380
  0.3  6          2        0.8651207  0.7249412  0.8623358
  0.3  6          3        0.8700773  0.7195294  0.8706569
  0.3  6          4        0.8706561  0.7249412  0.8737226
  0.3  6          5        0.8747883  0.7263529  0.8725547
  0.3  6          6        0.8765693  0.7282353  0.8747445
  0.3  6          7        0.8763426  0.7263529  0.8792701
  0.3  7          2        0.8645762  0.7232941  0.8600000
  0.3  7          3        0.8698042  0.7237647  0.8718248
  0.3  7          4        0.8714453  0.7261176  0.8745985
  0.3  7          5        0.8736239  0.7289412  0.8781022
  0.3  7          6        0.8749987  0.7282353  0.8797080
  0.3  7          7        0.8763650  0.7277647  0.8818978
  0.5  2          2        0.8564156  0.6658824  0.8794161
  0.5  2          3        0.8625985  0.7120000  0.8684672
  0.5  2          4        0.8649206  0.7275294  0.8636496
  0.5  2          5        0.8681580  0.7296471  0.8654015
  0.5  2          6        0.8687978  0.7232941  0.8667153
  0.5  2          7        0.8699382  0.7171765  0.8722628
  0.5  3          2        0.8685307  0.7247059  0.8703650
  0.5  3          3        0.8717518  0.7277647  0.8694891
  0.5  3          4        0.8731404  0.7218824  0.8767883
  0.5  3          5        0.8726930  0.7129412  0.8820438
  0.5  3          6        0.8728278  0.7190588  0.8811679
  0.5  3          7        0.8734865  0.7202353  0.8827737
  0.5  4          2        0.8676591  0.7294118  0.8646715
  0.5  4          3        0.8718987  0.7197647  0.8759124
  0.5  4          4        0.8709841  0.7202353  0.8753285
  0.5  4          5        0.8709901  0.7195294  0.8792701
  0.5  4          6        0.8713242  0.7195294  0.8781022
  0.5  4          7        0.8717192  0.7225882  0.8811679
  0.5  5          2        0.8669815  0.7152941  0.8694891
  0.5  5          3        0.8718824  0.7155294  0.8750365
  0.5  5          4        0.8732769  0.7209412  0.8786861
  0.5  5          5        0.8747763  0.7183529  0.8821898
  0.5  5          6        0.8748802  0.7188235  0.8805839
  0.5  5          7        0.8744139  0.7242353  0.8797080
  0.5  6          2        0.8671687  0.7202353  0.8700730
  0.5  6          3        0.8718360  0.7192941  0.8756204
  0.5  6          4        0.8734650  0.7164706  0.8816058
  0.5  6          5        0.8738386  0.7228235  0.8814599
  0.5  6          6        0.8731095  0.7188235  0.8836496
  0.5  6          7        0.8732761  0.7261176  0.8824818
  0.5  7          2        0.8665393  0.7261176  0.8664234
  0.5  7          3        0.8709807  0.7265882  0.8750365
  0.5  7          4        0.8726621  0.7275294  0.8781022
  0.5  7          5        0.8721889  0.7320000  0.8783942
  0.5  7          6        0.8733568  0.7320000  0.8798540
  0.5  7          7        0.8729841  0.7348235  0.8804380

Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter
 'colsample_bytree' was held constant at a value of 1
Tuning parameter 'min_child_weight'
 was held constant at a value of 1
Tuning parameter 'subsample' was held constant at a value of 1
ROC was used to select the optimal model using  the largest value.
The final values used for the model were nrounds = 6, max_depth = 6, eta = 0.3, gamma =
 1, colsample_bytree = 1, min_child_weight = 1 and subsample = 1.
plot(xgbFit.LGOCV)

xgb.importance(feature_names = colnames(Dtrain),model = xgbFit.LGOCV$finalModel) %>%
    xgb.ggplot.importance()

densityplot(xgbFit.LGOCV,pch='|')

predict(xgbFit.LGOCV,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)

Extreme Gradient Boosting (xgboost) - SMOTE Sampling

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
variable 'Survived' is not a factor
set.seed(1)
xgbsmoteFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 

Attaching package: ‘DMwR’

The following object is masked from ‘package:plyr’:

    join
- Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
Aggregating results
Selecting tuning parameters
Fitting nrounds = 7, max_depth = 6, eta = 0.5, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbsmoteFit
eXtreme Gradient Boosting 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Addtional sampling using SMOTE

Resampling results across tuning parameters:

  eta  max_depth  nrounds  ROC        Sens       Spec     
  0.3  2          2        0.8421146  0.5939328  0.9082559
  0.3  2          3        0.8483595  0.6400000  0.9024242
  0.3  2          4        0.8551781  0.6580000  0.9017037
  0.3  2          5        0.8609146  0.6563697  0.9038519
  0.3  2          6        0.8649248  0.6597983  0.9046061
  0.3  2          7        0.8670126  0.6550756  0.9093670
  0.3  3          2        0.8613064  0.6313109  0.9162694
  0.3  3          3        0.8639470  0.6114118  0.9224579
  0.3  3          4        0.8653315  0.6101513  0.9257374
  0.3  3          5        0.8665421  0.6055630  0.9260943
  0.3  3          6        0.8696859  0.6037983  0.9271785
  0.3  3          7        0.8716434  0.6007563  0.9322761
  0.3  4          2        0.8609969  0.6036471  0.9311919
  0.3  4          3        0.8652289  0.6163866  0.9301347
  0.3  4          4        0.8688009  0.6158655  0.9366801
  0.3  4          5        0.8726035  0.6210924  0.9388485
  0.3  4          6        0.8751455  0.6099328  0.9414007
  0.3  4          7        0.8749105  0.6128908  0.9395758
  0.3  5          2        0.8601735  0.6023193  0.9333603
  0.3  5          3        0.8610160  0.5988908  0.9344444
  0.3  5          4        0.8667343  0.6082017  0.9362896
  0.3  5          5        0.8706154  0.6145546  0.9311785
  0.3  5          6        0.8722259  0.6197815  0.9308215
  0.3  5          7        0.8737761  0.6244538  0.9322828
  0.3  6          2        0.8580192  0.6235462  0.9227946
  0.3  6          3        0.8583093  0.6247059  0.9216902
  0.3  6          4        0.8626271  0.6206387  0.9253333
  0.3  6          5        0.8661660  0.6339832  0.9268013
  0.3  6          6        0.8690932  0.6369412  0.9264242
  0.3  6          7        0.8716388  0.6515966  0.9213199
  0.3  7          2        0.8611640  0.6346723  0.9169630
  0.3  7          3        0.8647727  0.6340000  0.9187811
  0.3  7          4        0.8666008  0.6405210  0.9213401
  0.3  7          5        0.8698512  0.6487059  0.9184242
  0.3  7          6        0.8701002  0.6504202  0.9165993
  0.3  7          7        0.8739733  0.6621008  0.9133199
  0.5  2          2        0.8488864  0.6380840  0.9082492
  0.5  2          3        0.8575909  0.6740336  0.8965791
  0.5  2          4        0.8642655  0.6347731  0.9137306
  0.5  2          5        0.8645471  0.6294286  0.9187946
  0.5  2          6        0.8705065  0.6159664  0.9221145
  0.5  2          7        0.8705196  0.6112605  0.9257172
  0.5  3          2        0.8628080  0.6190588  0.9224242
  0.5  3          3        0.8668252  0.6037311  0.9264444
  0.5  3          4        0.8698613  0.5954454  0.9293333
  0.5  3          5        0.8718494  0.6077311  0.9304310
  0.5  3          6        0.8749923  0.6100672  0.9344310
  0.5  3          7        0.8756202  0.6142521  0.9406195
  0.5  4          2        0.8621987  0.5960504  0.9432189
  0.5  4          3        0.8711300  0.6007395  0.9373872
  0.5  4          4        0.8760000  0.6077815  0.9399192
  0.5  4          5        0.8751627  0.6269916  0.9344781
  0.5  4          6        0.8740639  0.6181345  0.9359192
  0.5  4          7        0.8735838  0.6247059  0.9341077
  0.5  5          2        0.8603518  0.6018319  0.9333872
  0.5  5          3        0.8689792  0.6158992  0.9308013
  0.5  5          4        0.8712972  0.6246218  0.9289832
  0.5  5          5        0.8732240  0.6350924  0.9249764
  0.5  5          6        0.8706622  0.6474118  0.9231515
  0.5  5          7        0.8716543  0.6520504  0.9220808
  0.5  6          2        0.8629268  0.6323025  0.9202357
  0.5  6          3        0.8680653  0.6421008  0.9213131
  0.5  6          4        0.8719936  0.6385882  0.9187811
  0.5  6          5        0.8722299  0.6532605  0.9158586
  0.5  6          6        0.8754213  0.6708235  0.9129293
  0.5  6          7        0.8787050  0.6673445  0.9129293
  0.5  7          2        0.8653486  0.6469580  0.9278788
  0.5  7          3        0.8679310  0.6545546  0.9195152
  0.5  7          4        0.8700789  0.6597479  0.9187879
  0.5  7          5        0.8746540  0.6708739  0.9202424
  0.5  7          6        0.8743795  0.6773613  0.9166061
  0.5  7          7        0.8771199  0.6796807  0.9165926

Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter
 'colsample_bytree' was held constant at a value of 1
Tuning parameter 'min_child_weight'
 was held constant at a value of 1
Tuning parameter 'subsample' was held constant at a value of 1
ROC was used to select the optimal model using  the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.5, gamma =
 1, colsample_bytree = 1, min_child_weight = 1 and subsample = 1.
plot(xgbsmoteFit)

xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbsmoteFit,pch='|')

predict(xgbsmoteFit,type = 'raw') -> train.Class
predict(xgbsmoteFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)

Extreme Gradient Boosting (xgboost) - down Sampling

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'down'
                     )
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
variable 'Survived' is not a factor
set.seed(1)
xgbdownFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
Aggregating results
Selecting tuning parameters
Fitting nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbdownFit
eXtreme Gradient Boosting 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Addtional sampling using down-sampling

Resampling results across tuning parameters:

  eta  max_depth  nrounds  ROC        Sens       Spec     
  0.3  2          2        0.8569065  0.7830756  0.8181953
  0.3  2          3        0.8644267  0.7929580  0.8189158
  0.3  2          4        0.8656720  0.7777311  0.8298855
  0.3  2          5        0.8675445  0.7737479  0.8342626
  0.3  2          6        0.8683614  0.7737143  0.8367811
  0.3  2          7        0.8696930  0.7860672  0.8269495
  0.3  3          2        0.8666484  0.7976303  0.8091582
  0.3  3          3        0.8731929  0.7964874  0.8240606
  0.3  3          4        0.8750420  0.7970756  0.8328418
  0.3  3          5        0.8754808  0.7982521  0.8339057
  0.3  3          6        0.8764200  0.7982353  0.8371717
  0.3  3          7        0.8748266  0.7988235  0.8368081
  0.3  4          2        0.8678127  0.8052605  0.8087071
  0.3  4          3        0.8752221  0.7981849  0.8236700
  0.3  4          4        0.8752527  0.8011261  0.8323973
  0.3  4          5        0.8761151  0.7976303  0.8309428
  0.3  4          6        0.8763856  0.8034958  0.8375017
  0.3  4          7        0.8763981  0.8028403  0.8415219
  0.3  5          2        0.8710305  0.8146555  0.8079865
  0.3  5          3        0.8742439  0.8059160  0.8170774
  0.3  5          4        0.8737292  0.8040504  0.8232727
  0.3  5          5        0.8756644  0.7993950  0.8294478
  0.3  5          6        0.8775969  0.7947227  0.8331178
  0.3  5          7        0.8784699  0.7930420  0.8353266
  0.3  6          2        0.8665360  0.7982521  0.8043502
  0.3  6          3        0.8726915  0.7913277  0.8171178
  0.3  6          4        0.8746105  0.7976639  0.8214949
  0.3  6          5        0.8771985  0.7930252  0.8320471
  0.3  6          6        0.8783720  0.7877311  0.8360539
  0.3  6          7        0.8799762  0.7895462  0.8324444
  0.3  7          2        0.8673833  0.7935798  0.8087205
  0.3  7          3        0.8695888  0.7942017  0.8119798
  0.3  7          4        0.8737363  0.7901008  0.8188956
  0.3  7          5        0.8750842  0.7953109  0.8269562
  0.3  7          6        0.8754110  0.7923866  0.8313131
  0.3  7          7        0.8763099  0.7930084  0.8338653
  0.5  2          2        0.8614709  0.7971092  0.8069428
  0.5  2          3        0.8649317  0.7908235  0.8171178
  0.5  2          4        0.8668110  0.7889244  0.8200673
  0.5  2          5        0.8661688  0.7901681  0.8197037
  0.5  2          6        0.8690462  0.8011597  0.8160606
  0.5  2          7        0.8721577  0.8057983  0.8182626
  0.5  3          2        0.8715849  0.7843529  0.8277239
  0.5  3          3        0.8746048  0.7895462  0.8280741
  0.5  3          4        0.8733359  0.7872269  0.8353670
  0.5  3          5        0.8725359  0.7948403  0.8302492
  0.5  3          6        0.8730766  0.7994958  0.8287879
  0.5  3          7        0.8733655  0.8047563  0.8251582
  0.5  4          2        0.8725090  0.7937311  0.8269764
  0.5  4          3        0.8752285  0.7929916  0.8288215
  0.5  4          4        0.8716937  0.7913109  0.8368013
  0.5  4          5        0.8714207  0.7825210  0.8382896
  0.5  4          6        0.8727354  0.7848739  0.8390303
  0.5  4          7        0.8722140  0.7843025  0.8364646
  0.5  5          2        0.8732848  0.8036134  0.8219192
  0.5  5          3        0.8758392  0.7924202  0.8222088
  0.5  5          4        0.8750053  0.7947395  0.8313535
  0.5  5          5        0.8734751  0.7942521  0.8335623
  0.5  5          6        0.8750399  0.7930252  0.8375556
  0.5  5          7        0.8742379  0.7913109  0.8375623
  0.5  6          2        0.8702289  0.7947563  0.8204108
  0.5  6          3        0.8712628  0.7907059  0.8317239
  0.5  6          4        0.8751280  0.7895798  0.8393603
  0.5  6          5        0.8743984  0.7901345  0.8371650
  0.5  6          6        0.8755410  0.7895630  0.8342694
  0.5  6          7        0.8771089  0.7912941  0.8357576
  0.5  7          2        0.8703945  0.8059832  0.8156970
  0.5  7          3        0.8707655  0.8035966  0.8240741
  0.5  7          4        0.8750083  0.8018319  0.8295286
  0.5  7          5        0.8769117  0.7988739  0.8364646
  0.5  7          6        0.8760763  0.8012773  0.8368350
  0.5  7          7        0.8761337  0.8006555  0.8331650

Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter
 'colsample_bytree' was held constant at a value of 1
Tuning parameter 'min_child_weight'
 was held constant at a value of 1
Tuning parameter 'subsample' was held constant at a value of 1
ROC was used to select the optimal model using  the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.3, gamma =
 1, colsample_bytree = 1, min_child_weight = 1 and subsample = 1.
plot(xgbdownFit)

xgb.importance(feature_names = colnames(Dtrain),model = xgbdownFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbdownFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbdownFit,pch='|')

predict(xgbdownFit,type = 'raw') -> train.Class
predict(xgbdownFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)

Gradient Boosting (gbm)

boostFit
Stochastic Gradient Boosting 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Resampling results across tuning parameters:

  shrinkage  interaction.depth  n.trees  ROC        Sens       Spec     
  0.01       1                   500     0.8687740  0.7176975  0.8812727
  0.01       1                   700     0.8715465  0.7310756  0.8827340
  0.01       1                   900     0.8731344  0.7456975  0.8798182
  0.01       1                  1100     0.8737417  0.7614454  0.8790909
  0.01       2                   500     0.8768379  0.7410252  0.8819933
  0.01       2                   700     0.8781806  0.7549916  0.8808956
  0.01       2                   900     0.8791223  0.7614118  0.8801751
  0.01       2                  1100     0.8801043  0.7613950  0.8809024
  0.01       3                   500     0.8790775  0.7526218  0.8823636
  0.01       3                   700     0.8808075  0.7584874  0.8812727
  0.01       3                   900     0.8815746  0.7549748  0.8809091
  0.01       3                  1100     0.8817104  0.7532269  0.8849091
  0.10       1                   500     0.8699484  0.7480168  0.8721751
  0.10       1                   700     0.8708636  0.7485882  0.8736162
  0.10       1                   900     0.8686151  0.7422017  0.8743367
  0.10       1                  1100     0.8689881  0.7428067  0.8725051
  0.10       2                   500     0.8801393  0.7473950  0.8837845
  0.10       2                   700     0.8799571  0.7467563  0.8819798
  0.10       2                   900     0.8790711  0.7438992  0.8790370
  0.10       2                  1100     0.8773468  0.7474118  0.8757778
  0.10       3                   500     0.8803987  0.7450252  0.8790774
  0.10       3                   700     0.8805280  0.7438655  0.8746869
  0.10       3                   900     0.8793306  0.7438655  0.8692189
  0.10       3                  1100     0.8785908  0.7432773  0.8713872

Tuning parameter 'n.minobsinnode' was held constant at a value of 10
ROC was used to select the optimal model using  the largest value.
The final values used for the model were n.trees = 1100, interaction.depth = 3, shrinkage
 = 0.01 and n.minobsinnode = 10.
plot(boostFit)

xyplot(oobag.improve~1:1100,data=boostFit$finalModel,alpha=.5,xlab = 'n.trees')

plot(varImp(boostFit))

densityplot(boostFit,pch='|')

predict(boostFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)

Random Forest (custom rf)


customRF <-
    list(type = "Classification",
    library = "randomForest",
    loop = NULL)

customRF$parameters <- data.frame(
    parameter = c("mtry", "ntree", 'nodesize', 'replace'),
    class = c("numeric", 'numeric', 'numeric', 'logical'),
    label = c("mtry", "ntree", 'nodesize', 'replace')
    )
customRF$grid <- function(x, y, len = NULL, search = "grid") {}

customRF$fit <-
    function(x,
    y,
    wts,
    param,
    lev,
    last,
    weights,
    classProbs,
    ...) {
    randomForest(
    x,
    y,
    mtry = param$mtry,
    ntree = param$ntree,
    replace = param$replace,
    # classwt = param$classwt,
    nodesize = param$nodesize
    )
    }
customRF$predict <-
    function(modelFit,
    newdata,
    preProc = NULL,
    submodels = NULL)
    predict(modelFit, newdata)

customRF$prob <-
    function(modelFit,
    newdata,
    preProc = NULL,
    submodels = NULL)
        predict(modelFit, newdata, type = "prob")
customRF$sort <- function(x)
    x[order(x[, 1]), ]

customRF$levels <- function(x)
    x$classes

customRF
    
    
ctrl <- trainControl(
    method = "repeatedcv",
    repeats = 5,
    verboseIter = T,
    classProbs = TRUE,
    summaryFunction = twoClassSummary,
    # sampling = 'smote'
)
crfGrid <- expand.grid(
    mtry = c(10, 15, 20, 29),
    ntree = c(100, 300, 500),
    nodesize = c(1, 5, 10),
    replace = c(T, F)
    # classwt = c(0.6, 0.8)
)
set.seed(1)
crfFit <- train(
    form = Survived ~ .,
    data = train.imp,
    method = customRF,
    trControl = adapt_ctrl,
    # metric = "Kappa",
    tuneGrid = crfGrid,
    verbose = TRUE,
    classwt = c(0.38, 0.61)
)
crfFit_class <- train(
    form = Survived ~ .,
    data = train.imp,
    method = customRF,
    trControl = adapt_ctrl,
    # metric = "Kappa",
    tuneGrid = crfGrid,
    verbose = TRUE,
    classwt = c(0.38, 0.61)
)
    

Random Forest (rf)

rfFit.y
Random Forest 

891 samples
 28 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Resampling results across tuning parameters:

  mtry  ROC        Sens       Spec     
   5    0.8842523  0.7122353  0.8969293
  10    0.8885360  0.7286891  0.8940202
  15    0.8874201  0.7351429  0.8878182
  20    0.8864149  0.7415462  0.8867138
  25    0.8866698  0.7427563  0.8816229

ROC was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 10.
plot(rfFit.y)

plot(rfFit.y$finalModel)

densityplot(rfFit.y,pch='|')

predict(rfFit.y,type = 'prob') -> train.rf.Probs
histogram(~Survived+Dead,train.rf.Probs)

Random Forest (rf) - SMOTE

rfsmoteFit.y
Random Forest 

891 samples
 28 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Addtional sampling using SMOTE

Resampling results across tuning parameters:

  mtry  ROC        Sens       Spec     
   5    0.8783818  0.6333782  0.9369966
  10    0.8821756  0.6854286  0.9129697
  15    0.8831274  0.6983025  0.9016498
  20    0.8814043  0.7129580  0.8910976
  25    0.8793606  0.7135462  0.8885455

ROC was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 15.
plot(rfsmoteFit.y)

plot(rfsmoteFit.y$finalModel)

densityplot(rfsmoteFit.y,pch='|')

predict(rfsmoteFit.y,type = 'prob') -> train.rfsmoteFit.Probs
histogram(~Survived+Dead,train.rfsmoteFit.Probs)

Elastinet

glmnetFit
glmnet 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Addtional sampling using SMOTE

Resampling results across tuning parameters:

  alpha  lambda      ROC        Sens       Spec     
  0.0    0.01000000  0.8624351  0.7269580  0.8875152
  0.0    0.01487179  0.8624351  0.7269580  0.8875152
  0.0    0.01974359  0.8624351  0.7269580  0.8875152
  0.0    0.02461538  0.8624351  0.7269580  0.8875152
  0.0    0.02948718  0.8624351  0.7269580  0.8875152
  0.0    0.03435897  0.8626287  0.7269580  0.8871515
  0.0    0.03923077  0.8629192  0.7269412  0.8871515
  0.0    0.04410256  0.8631763  0.7269076  0.8860539
  0.0    0.04897436  0.8633421  0.7263361  0.8856902
  0.0    0.05384615  0.8631602  0.7246218  0.8860539
  0.0    0.05871795  0.8632236  0.7234286  0.8856902
  0.0    0.06358974  0.8634279  0.7240168  0.8853266
  0.0    0.06846154  0.8634157  0.7240168  0.8838721
  0.0    0.07333333  0.8634152  0.7246218  0.8835017
  0.0    0.07820513  0.8633267  0.7252269  0.8838653
  0.0    0.08307692  0.8633870  0.7246723  0.8842290
  0.0    0.08794872  0.8632672  0.7264370  0.8838653
  0.0    0.09282051  0.8630221  0.7258487  0.8838653
  0.0    0.09769231  0.8630112  0.7246723  0.8835017
  0.0    0.10256410  0.8630108  0.7258487  0.8827744
  0.0    0.10743590  0.8629793  0.7270252  0.8820337
  0.0    0.11230769  0.8629057  0.7275966  0.8820337
  0.0    0.11717949  0.8629160  0.7264370  0.8816700
  0.0    0.12205128  0.8629367  0.7252605  0.8816700
  0.0    0.12692308  0.8628505  0.7246723  0.8813064
  0.0    0.13179487  0.8627742  0.7252437  0.8813064
  0.0    0.13666667  0.8626347  0.7240840  0.8816700
  0.0    0.14153846  0.8626660  0.7234790  0.8816700
  0.0    0.14641026  0.8624861  0.7228908  0.8816700
  0.0    0.15128205  0.8623359  0.7205378  0.8809360
  0.0    0.15615385  0.8622288  0.7211261  0.8805724
  0.0    0.16102564  0.8621643  0.7211092  0.8798451
  0.0    0.16589744  0.8620677  0.7199496  0.8791111
  0.0    0.17076923  0.8620130  0.7193782  0.8787475
  0.0    0.17564103  0.8620221  0.7193782  0.8791111
  0.0    0.18051282  0.8620338  0.7193782  0.8791111
  0.0    0.18538462  0.8620142  0.7193782  0.8780202
  0.0    0.19025641  0.8620445  0.7182017  0.8765657
  0.0    0.19512821  0.8619043  0.7170252  0.8765657
  0.0    0.20000000  0.8618487  0.7152773  0.8758384
  0.1    0.01000000  0.8600372  0.7241176  0.8878519
  0.1    0.01487179  0.8615477  0.7264538  0.8896768
  0.1    0.01974359  0.8624891  0.7241345  0.8875017
  0.1    0.02461538  0.8631904  0.7223866  0.8878653
  0.1    0.02948718  0.8635738  0.7270252  0.8871246
  0.1    0.03435897  0.8640134  0.7281513  0.8867542
  0.1    0.03923077  0.8642901  0.7257983  0.8860269
  0.1    0.04410256  0.8644447  0.7257983  0.8852997
  0.1    0.04897436  0.8644893  0.7246218  0.8852997
  0.1    0.05384615  0.8647542  0.7234622  0.8845724
  0.1    0.05871795  0.8648624  0.7205378  0.8849360
  0.1    0.06358974  0.8646418  0.7205378  0.8849360
  0.1    0.06846154  0.8645368  0.7211261  0.8849360
  0.1    0.07333333  0.8643896  0.7193950  0.8842020
  0.1    0.07820513  0.8643594  0.7176303  0.8842020
  0.1    0.08307692  0.8642748  0.7141008  0.8845589
  0.1    0.08794872  0.8640778  0.7135294  0.8841953
  0.1    0.09282051  0.8636771  0.7123697  0.8834815
  0.1    0.09769231  0.8635362  0.7129580  0.8823906
  0.1    0.10256410  0.8631628  0.7123697  0.8805657
  0.1    0.10743590  0.8628222  0.7123529  0.8798316
  0.1    0.11230769  0.8625770  0.7106050  0.8794680
  0.1    0.11717949  0.8622052  0.7111933  0.8780135
  0.1    0.12205128  0.8619550  0.7111933  0.8772795
  0.1    0.12692308  0.8616472  0.7100336  0.8765522
  0.1    0.13179487  0.8616144  0.7088908  0.8761886
  0.1    0.13666667  0.8614748  0.7083025  0.8747340
  0.1    0.14153846  0.8612211  0.7082857  0.8747340
  0.1    0.14641026  0.8611024  0.7076975  0.8743636
  0.1    0.15128205  0.8609678  0.7065210  0.8740000
  0.1    0.15615385  0.8605709  0.7059328  0.8732727
  0.1    0.16102564  0.8604414  0.7059496  0.8725455
  0.1    0.16589744  0.8602385  0.7048067  0.8721751
  0.1    0.17076923  0.8599341  0.7036471  0.8725387
  0.1    0.17564103  0.8598033  0.7042353  0.8718114
  0.1    0.18051282  0.8595210  0.7036639  0.8707205
  0.1    0.18538462  0.8591656  0.7024874  0.8703569
  0.1    0.19025641  0.8589091  0.7018992  0.8699933
  0.1    0.19512821  0.8584617  0.7013277  0.8703569
  0.1    0.20000000  0.8581420  0.7013277  0.8692660
  0.2    0.01000000  0.8591220  0.7280672  0.8882020
  0.2    0.01487179  0.8604820  0.7245882  0.8903906
  0.2    0.01974359  0.8618651  0.7228571  0.8911178
  0.2    0.02461538  0.8620406  0.7228908  0.8892997
  0.2    0.02948718  0.8620432  0.7217143  0.8889360
  0.2    0.03435897  0.8622358  0.7234958  0.8889428
  0.2    0.03923077  0.8622652  0.7223361  0.8885859
  0.2    0.04410256  0.8620627  0.7229412  0.8889495
  0.2    0.04897436  0.8620116  0.7188235  0.8874949
  0.2    0.05384615  0.8619238  0.7153109  0.8864040
  0.2    0.05871795  0.8619083  0.7147227  0.8856700
  0.2    0.06358974  0.8614219  0.7141513  0.8853064
  0.2    0.06846154  0.8613769  0.7094790  0.8845724
  0.2    0.07333333  0.8613267  0.7089076  0.8849360
  0.2    0.07820513  0.8613301  0.7071429  0.8842088
  0.2    0.08307692  0.8613348  0.7065546  0.8823906
  0.2    0.08794872  0.8612440  0.7036471  0.8805724
  0.2    0.09282051  0.8607954  0.7036639  0.8791111
  0.2    0.09769231  0.8604293  0.7007563  0.8765657
  0.2    0.10256410  0.8599402  0.7007563  0.8747475
  0.2    0.10743590  0.8591161  0.7025378  0.8736431
  0.2    0.11230769  0.8580974  0.7019496  0.8721818
  0.2    0.11717949  0.8576971  0.7025042  0.8710774
  0.2    0.12205128  0.8571679  0.7025042  0.8703502
  0.2    0.12692308  0.8567174  0.7024874  0.8688956
  0.2    0.13179487  0.8561708  0.7007395  0.8656229
  0.2    0.13666667  0.8550908  0.7013277  0.8648956
  0.2    0.14153846  0.8544119  0.7007395  0.8645320
  0.2    0.14641026  0.8540218  0.7007563  0.8641684
  0.2    0.15128205  0.8534856  0.7007563  0.8638047
  0.2    0.15615385  0.8528506  0.7001681  0.8627138
  0.2    0.16102564  0.8523328  0.6984034  0.8627138
  0.2    0.16589744  0.8514521  0.6984034  0.8619865
  0.2    0.17076923  0.8508972  0.6978151  0.8598047
  0.2    0.17564103  0.8506594  0.6972269  0.8594411
  0.2    0.18051282  0.8500216  0.6978151  0.8583434
  0.2    0.18538462  0.8495585  0.6972437  0.8572458
  0.2    0.19025641  0.8490212  0.6966723  0.8561549
  0.2    0.19512821  0.8488122  0.6955126  0.8557912
  0.2    0.20000000  0.8477981  0.6949244  0.8547003
  0.4    0.01000000  0.8608629  0.7170924  0.8856835
  0.4    0.01487179  0.8630875  0.7188235  0.8875017
  0.4    0.01974359  0.8635522  0.7235126  0.8882290
  0.4    0.02461538  0.8638852  0.7241008  0.8863973
  0.4    0.02948718  0.8640961  0.7246387  0.8874882
  0.4    0.03435897  0.8636815  0.7205882  0.8878519
  0.4    0.03923077  0.8634645  0.7188403  0.8867542
  0.4    0.04410256  0.8625804  0.7165210  0.8845589
  0.4    0.04897436  0.8610777  0.7129916  0.8845589
  0.4    0.05384615  0.8603462  0.7106218  0.8812727
  0.4    0.05871795  0.8593766  0.7100168  0.8783569
  0.4    0.06358974  0.8586905  0.7106387  0.8758047
  0.4    0.06846154  0.8577189  0.7083193  0.8739865
  0.4    0.07333333  0.8561190  0.7071765  0.8707138
  0.4    0.07820513  0.8539432  0.7042521  0.8692525
  0.4    0.08307692  0.8520797  0.7048571  0.8674276
  0.4    0.08794872  0.8510941  0.7054454  0.8652391
  0.4    0.09282051  0.8498438  0.7060168  0.8612391
  0.4    0.09769231  0.8489821  0.7042521  0.8590572
  0.4    0.10256410  0.8485292  0.7054286  0.8543232
  0.4    0.10743590  0.8478570  0.7042521  0.8535960
  0.4    0.11230769  0.8474843  0.7013277  0.8514074
  0.4    0.11717949  0.8474952  0.7001345  0.8514141
  0.4    0.12205128  0.8472912  0.6995462  0.8503232
  0.4    0.12692308  0.8465502  0.6989580  0.8499596
  0.4    0.13179487  0.8458615  0.6977983  0.8495960
  0.4    0.13666667  0.8460465  0.6960336  0.8499596
  0.4    0.14153846  0.8456487  0.6936807  0.8510640
  0.4    0.14641026  0.8457106  0.6919328  0.8510640
  0.4    0.15128205  0.8463243  0.6884034  0.8514276
  0.4    0.15615385  0.8466792  0.6872269  0.8514276
  0.4    0.16102564  0.8467969  0.6860840  0.8514276
  0.4    0.16589744  0.8465443  0.6860840  0.8517912
  0.4    0.17076923  0.8460047  0.6854958  0.8521549
  0.4    0.17564103  0.8456889  0.6843193  0.8521549
  0.4    0.18051282  0.8452321  0.6843193  0.8521549
  0.4    0.18538462  0.8443937  0.6831765  0.8525185
  0.4    0.19025641  0.8439948  0.6831765  0.8525185
  0.4    0.19512821  0.8432182  0.6831765  0.8525185
  0.4    0.20000000  0.8429886  0.6820000  0.8525185
  0.6    0.01000000  0.8615146  0.7217311  0.8889428
  0.6    0.01487179  0.8625244  0.7234118  0.8911380
  0.6    0.01974359  0.8627385  0.7234622  0.8911246
  0.6    0.02461538  0.8630850  0.7211765  0.8900202
  0.6    0.02948718  0.8622451  0.7194286  0.8860000
  0.6    0.03435897  0.8620347  0.7135966  0.8830842
  0.6    0.03923077  0.8608737  0.7077311  0.8808956
  0.6    0.04410256  0.8595263  0.7077647  0.8776229
  0.6    0.04897436  0.8580028  0.7071765  0.8750774
  0.6    0.05384615  0.8563695  0.7054454  0.8725253
  0.6    0.05871795  0.8546962  0.7048739  0.8641616
  0.6    0.06358974  0.8528297  0.7066050  0.8601549
  0.6    0.06846154  0.8511820  0.7071765  0.8568822
  0.6    0.07333333  0.8498130  0.7089244  0.8521481
  0.6    0.07820513  0.8489291  0.7083361  0.8488687
  0.6    0.08307692  0.8492258  0.7071597  0.8488620
  0.6    0.08794872  0.8491236  0.7054118  0.8488620
  0.6    0.09282051  0.8492666  0.7018824  0.8488620
  0.6    0.09769231  0.8486469  0.7001176  0.8492256
  0.6    0.10256410  0.8485608  0.6954790  0.8506869
  0.6    0.10743590  0.8486213  0.6937143  0.8506869
  0.6    0.11230769  0.8478946  0.6931261  0.8506869
  0.6    0.11717949  0.8466370  0.6901849  0.8506869
  0.6    0.12205128  0.8461776  0.6860840  0.8517912
  0.6    0.12692308  0.8455451  0.6860840  0.8517912
  0.6    0.13179487  0.8436514  0.6854958  0.8517912
  0.6    0.13666667  0.8430642  0.6837647  0.8521549
  0.6    0.14153846  0.8423842  0.6831765  0.8521549
  0.6    0.14641026  0.8418510  0.6825882  0.8525185
  0.6    0.15128205  0.8407780  0.6820000  0.8525185
  0.6    0.15615385  0.8404541  0.6814118  0.8525185
  0.6    0.16102564  0.8401007  0.6814118  0.8525185
  0.6    0.16589744  0.8396466  0.6814118  0.8525185
  0.6    0.17076923  0.8391832  0.6814118  0.8525185
  0.6    0.17564103  0.8392284  0.6814118  0.8525185
  0.6    0.18051282  0.8390840  0.6814118  0.8525185
  0.6    0.18538462  0.8388220  0.6814118  0.8525185
  0.6    0.19025641  0.8389099  0.6814118  0.8525185
  0.6    0.19512821  0.8367532  0.6814118  0.8525185
  0.6    0.20000000  0.8350971  0.6814118  0.8525185
 [ reached getOption("max.print") -- omitted 80 rows ]

ROC was used to select the optimal model using  the largest value.
The final values used for the model were alpha = 0.1 and lambda = 0.05871795.
plot(glmnetFit,plotType='level')

plot(varImp(glmnetFit))

densityplot(glmnetFit,pch='|')

predict(glmnetFit,type = 'prob') -> train.glmnet.Probs
histogram(~Survived+Dead,train.glmnet.Probs)

Compare models

re <-
    resamples(
    x = list(
    xgb = xgbFit,
    xgbsmote = xgbsmoteFit,
    xgbdown = xgbdownFit,
    rf = rfFit.y,
    rfsmote = rfsmoteFit.y,
    gbm = boostFit,
    elastinet=glmnetFit,
    crf = crfFit,
    crfFit_class= crfFit_class
    )
    )
summary(re)
bwplot(re)
summary(diff(re))

Calibration curves…

simulatedTrain <- data.frame(Class = train.imp$Survived)
simulatedTrain$rf = predict(rfFit.y,type = 'prob')[[1]]
simulatedTrain$rfsmote = predict(rfsmoteFit.y,type = 'prob')[[1]]
simulatedTrain$xgb = predict(xgbFit,type = 'prob')[[1]]
simulatedTrain$xgbsmote = predict(xgbsmoteFit,type = 'prob')[[1]]
simulatedTrain$boost = predict(boostFit,type = 'prob')[[1]]
calCurve <- calibration(x = Class~rf+rfsmote+xgb+xgbsmote+boost,data = simulatedTrain)
xyplot(calCurve,auto.key=list(columns=3))

Calibrating probabilities

boostsigmoidCal <- glm(relevel(Class,ref='Dead')~boost,simulatedTrain,family = 'binomial')
coef(summary(boostsigmoidCal))
             Estimate Std. Error   z value     Pr(>|z|)
(Intercept) -3.393991  0.1992172 -17.03664 4.392812e-65
boost        7.063846  0.4064620  17.37886 1.193056e-67
simulatedTrain$boostSig = predict(boostsigmoidCal,type = 'response')
xgbsmotesigmoidCal <- glm(relevel(Class,ref='Dead')~xgbsmote,simulatedTrain,family = 'binomial')
coef(summary(xgbsmotesigmoidCal))
             Estimate Std. Error   z value     Pr(>|z|)
(Intercept) -3.468413  0.2016514 -17.20004 2.653386e-66
xgbsmote     9.205386  0.5945264  15.48356 4.479596e-54
simulatedTrain$xgbsmoteSig = predict(xgbsmotesigmoidCal,type = 'response')
calibration(x = Class~boost+boostSig,data = simulatedTrain) %>%
    xyplot(auto.key=list(columns=2))

calibration(x = Class~xgbsmote+xgbsmoteSig,data = simulatedTrain) %>%
    xyplot(auto.key=list(columns=2))

Test Set Evaluation

Create test set

Predict test results

xgbsmotesigmoidCal

Call:  glm(formula = relevel(Class, ref = "Dead") ~ xgbsmote, family = "binomial", 
    data = simulatedTrain)

Coefficients:
(Intercept)     xgbsmote  
     -3.468        9.205  

Degrees of Freedom: 890 Total (i.e. Null);  889 Residual
Null Deviance:      1187 
Residual Deviance: 491.9    AIC: 495.9

Write results

PID <-
    readData(Titanic.path,
    test.data.file,
    test.column.types,
    missing.types)
PID <- PID$PassengerId
for(m in ls(pattern = 'Pred')) {
    write.csv(
    x = data.frame(
    PassengerId = PID,
    Survived = as.numeric(eval(parse(text = m))) * -1 + 2
    ),
    file = paste0(m,'.csv'),
    row.names = F
    )
}

Conclusions

Misc - delete later…

d %>% reshape2::melt(value.name = 'value') %>%
    rownames_to_column(var = 'rowid') %>% 
    ggplot(aes(x=rowid, y=value))+
    geom_point(aes(col=variable))
No id variables; using all as measure variables

  1. I think this approach depends on the academic background and the industry of the analyst. Prof Srinivasan, and my mentor at work both have strong statistical academic backgrounds, and both believe in thorough EDA of the data. I’ve also noticed this approach from individuals in the banking & insurance industry - perhaps due to regulatory requirements. On the other hand, folks trained in computer science and algorithmic data science tend to underplay the importance of thorough EDA.

  2. To iterate variable names in ggplot, use ggplot(...)+aes_string(...) in place of ggplot(...,aes(...)).

  3. Read more about beanplots here: https://cran.r-project.org/web/packages/beanplot/vignettes/beanplot.pdf

---
title: "Kaggle Titanic Competition - Data Exploration & Model Building"
author: 'Rahul Sangole'
date: Aug 31, 2017
output:
  html_notebook:
    code_folding: hide
    collapsed: no
    fig_height: 3
    fig_width: 5
    highlight: pygments
    theme: journal
    toc: yes
    toc_float: yes
---
# Objectives

1. End to end analysis using R
2. Learn the caret package for ML
3. Learn to present the case using R Notebooks

***

# Read in the dataset
I stored the raw files on Github, so I used [RCurl](https://cran.r-project.org/web/packages/RCurl/index.html) with [Wehrley's method](https://github.com/wehrley/wehrley.github.io/blob/master/SOUPTONUTS.md) that utilizes read.csv to the fullest. It's one of the best ways I've found to read in data and also set data-types at the same time. He's done a great job on that function. The dataset contains one ID variable, one response variable and ten predictor variables.

```{r, message=FALSE, warning=FALSE}
library(RCurl,quietly = T)
library(tidyverse,quietly = T)
library(ggplot2,quietly = T)
library(gridExtra,quietly = T)
library(Amelia,quietly = T)
library(beanplot,quietly = T)
library(caret,quietly = T)
library(stringr,quietly = T)
library(party, quietly = T)
# library(rattle, quietly = T)

readData <- function(path.name, file.name, column.types, missing.types) {
    gurl <- paste(path.name,file.name,sep="")
    download.file(gurl,file.name,method="curl",quiet = T)
    tbl_df(read.csv(file.name,colClasses=column.types,
             na.strings=missing.types))
}

Titanic.path <- "https://raw.githubusercontent.com/rsangole/Titanic/master/"
train.data.file <- "train.csv"
test.data.file <- "test.csv"
missing.types <- c("NA", "")
train.column.types <- c('integer',   # PassengerId
                        'factor',    # Survived
                        'factor',    # Pclass
                        'character', # Name
                        'factor',    # Sex
                        'numeric',   # Age
                        'integer',   # SibSp
                        'integer',   # Parch
                        'character', # Ticket
                        'numeric',   # Fare
                        'character', # Cabin
                        'factor'     # Embarked
)

test.column.types <- train.column.types[-2]     # # no Survived column in test.csv
train.raw <- readData(Titanic.path, train.data.file,train.column.types,missing.types)
test.raw <- readData(Titanic.path, test.data.file,test.column.types,missing.types)

prep_data <- function(D) {
    if (!is.null(D$Survived)) {
        D$Survived <- factor(D$Survived,
                             levels = c(1, 0),
                             labels = c('Survived', 'Dead'))
        }
    D$Pclass <- factor(D$Pclass,
                       levels = c(1, 2, 3),
                       labels = c('P1', 'P2', 'P3'))
    D$PassengerId <- NULL
    D
}

train.raw <- prep_data(train.raw)
test.raw <- prep_data(test.raw)
str(train.raw)
```

***

# Missing values analysis

Quick investigation of missing values can be done using the `complete.cases()`, and more thorough graphical summary can be done using Amelia. Overall, 79% of the observations have *some* missing data.

```{r}
#Complete cases (percentages)
round(prop.table(table(complete.cases(train.raw))),2)
```
Amelia lets us graphically investigate which variables have missing data. `purr::map_xxx()` gives this same information numerically in a succint fashion.
```{r, message=FALSE, warning=FALSE}
missmap(train.raw, main='Missing Values Analysis using Amelia ordered by % missing', col=c('red', 'gray'),legend = F,rank.order = T)
#Missing cases (numbers):
map_int(train.raw,~sum(is.na(.x)))
#Missing cases (percentages):
round(map_dbl(train.raw,~sum(is.na(.x))/length(.x)),2)
```
Cabin has a large number of missing values (77% missing). Imputing this variable may prove challenging or even useless. Age (19.9% missing) and Embarked (0.2%) missing are much more managable.

***

# EDA

The first step in the analysis is to explore the data numerically and graphically. I always split up my EDA investigation as follows:

* Target Variable
* Predictor Variables
    + Univariate
    + Bivariate
    + Multivariate

This gives me a structured approach towards larger datasets. My [professor](http://www.syamalasrinivasan.com/) at Northwestern taught me to always complete a thorough intimate numeric & graphical EDA on the data, no matter how large the data [^1]. [Anscombe](http://www.jstor.org/stable/2682899) (1973) clearly shows the importance of graphical analyses.

[^1]: I think this approach depends on the academic background and the industry of the analyst. Prof Srinivasan, and my mentor at work both have strong statistical academic backgrounds, and both believe in thorough EDA of the data. I've also noticed this approach from individuals in the banking & insurance industry - perhaps due to regulatory requirements. On the other hand, folks trained in computer science and algorithmic data science tend to underplay the importance of thorough EDA.

## Target Variable
`Survived` is the response variable. As we can see, a large majority of the passengers did not survive the accident. The response variable is a False/True boolean variable. Thus, the analysis techniques used later will be those appropriate for classification problems.
```{r}
round(prop.table(table(train.raw$Survived)),2)
```

***

## Predictor Variables {.tabset .tabset-fade}

### Univariate & Bivariate

The first step is to look at every variable available. I prefer using the `ggplot2` framework for all the visuals.

#### Continuous Variables

* `Age` seems to have a bimodal distribution - very young children, and then directly young adults to mid-age persons. The 2nd mode is right skewed with no obvious outliers.

* `Fare` certainly shows many outliers beyond the ~$200 level. A majority of the fares are <$50, which makes sense since a majority of the travelers are bound to be in the 3rd passenger class.

```{r, message=FALSE, warning=FALSE}
p1 <- ggplot(data=train.raw,aes(x=Age))+geom_histogram(bins = 40)
p2 <- ggplot(data=train.raw,aes(x=Fare))+geom_histogram(bins = 40)
grid.arrange(p1,p2)
```

As we can see, the median fare is $14.5, the mean is $32, but the max is $512. We'll investigate winzorising this variable in the latter part. Perhaps a transformation will also help?

```{r}
summary(train.raw$Fare)
```

#### Categorical Variables

A ggplot command is iterated over for the categorical variables.[^2]

[^2]: To iterate variable names in ggplot, use `ggplot(...)+aes_string(...)` in place of `ggplot(...,aes(...))`.

Key takeways for the categorical variables:

1. `Pclass`: If you were traveling 1st class, you have the highest chance of survival. Could be indicative of preferential treatment to those who paid more, a less politically correct class-stratified society, as well as the fact that the 1st class passengers had cabins at the very top of the ship.
2. `Pclass`: Persons traveling 3rd class had the highest fatality rate. 3rd class passengers had cabins deep in the ship. With the reasons give in (1), this could have contributed to the low survival rate.
3. `Sex`: Males have a very high fatality rate. Seems like the 'women and children' first policy was followed during evacuation.
4. `SibSp` & `Parch`: What's interesting here is, for both these variables, at level 0, the fatality rate is higher. At levels 1+, the chances of survival are much better. Again, this could point to the 'women *and children*' policy being followed. (Or perhaps there weren't as many families with children on board!)
6. `Embarked`: Southampton has a higher fatality rate than Cherbourg or Queenstown. A cross-tabulation between `Embarked` and `Pclass` shows that 72% of the 3rd class passengers and 89% of the 2nd class passengers boarded at Southampton. This jives with the observation that 2nd and 3rd class passengers have higher fatality rates.

```{r, message=FALSE, warning=FALSE}
get_legend<-function(myggplot){
  tmp <- ggplot_gtable(ggplot_build(myggplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)
}
p <- lapply(X = c('Pclass','Sex','SibSp','Parch','Embarked'),
            FUN = function(x) ggplot(data = train.raw)+
                aes_string(x=x,fill='Survived')+
                geom_bar(position="dodge")+
                theme(legend.position="none"))
legend <- get_legend(ggplot(data = train.raw,aes(x=Pclass,fill=Survived))+geom_bar())
grid.arrange(p[[1]],p[[2]],p[[3]],p[[4]],p[[5]],legend,layout_matrix =
                 cbind(c(1,2,3),c(4,5,NA),c(6,6,6)),widths=c(3,3,1))
# round(prop.table(table(train.raw$Embarked,train.raw$Pclass),margin = 2),2)
```

### Multivariate Analyses

Grouped boxplots are a common method of comparing distributions grouped by categorical variables. I find [beanplots](https://cran.r-project.org/web/packages/beanplot/beanplot.pdf) to be excellent complementary plots to boxplots (and in some cases, even better). They're a bit tricky to read at first - since they are so underutilized - but just through one plot, a wealth of information can be extracted.[^3]

Here is a comparison of the same information between a boxplot and a beanplot. What can we infer from the bean plot better?

1. The beanplot allows us to visualize the density function of the parameter, in this case: Age. Furthermore, the length of each beanline is cumulative to the number of datapoints that exist. Rightaway, we can tell that Pclass=3 has the most data in the set, with sparser data at Pclass=1.
2. The mean values for 1st class is higher than that for 2nd and 3rd class. The distributions of deceased and survived for 1st class are fairly similar.
3. For 2nd and 3rd class, the survived data shows a bimodal distribution. Bumps at the 0-10 age show that children were evacuated first. This is also the reason the mean values for survived is lower.
4. For 2nd and 3rd class, the deceased data shows a fairly normal distribution.
5. The individual measurements (represented by black lines) represent each observation and help identify outliers much more easily than a boxplot does.

[^3]: Read more about beanplots here: https://cran.r-project.org/web/packages/beanplot/vignettes/beanplot.pdf

```{r, fig.height=3, fig.width=5, message=FALSE, warning=FALSE}
ggplot(train.raw,aes(y=Age,x=Pclass))+geom_boxplot(aes(fill=Survived))+theme_bw()
beanplot(Age~Survived*Pclass,side='b',train.raw,col=list('yellow','orange'),
         border = c('yellow2','darkorange'),ll = 0.05,boxwex = .5,
         main='Passenger survival by pclass and Age',xlab='Passenger Class',ylab='Age')
legend('topright', fill = c('yellow','orange'), legend = c("Dead", "Survived"),bty = 'n',cex = .8)
```

A look into the `SibSp` and `Parch` variables shows something interesting. There are three regions one can identify:

* The probability of survival is minimal for number of parents/children aboard > 3.
* The probability of survival is minimal for number of siblings/spouses aboard > 3.
* For `SibSp`<=3 and `Parch`<=3, there are better chances for survival.

The grouping by `Pclass` reveals that all the large families were 3rd class travelers. Worse access to help... lowest chance for survival.

These could be simple rules either hard coded during model building: something along the lines of: *IF (SibSp>3 OR Parch >3) THEN prediction = 0*, or some derived variables can be created.

```{r}
ggplot(train.raw,aes(y=SibSp,x=Parch))+
    geom_jitter(aes(color=Survived,shape=Pclass))+
    theme_bw()+
    scale_shape(solid=F)+
    geom_vline(xintercept = 3,color='darkblue',lty=3)+
    geom_hline(yintercept = 3,color='darkblue',lty=3)
```

***

# Data Preparation
## Missing Values Imputation
Starting with the easier one first:

**Embarked**: The largest portion of the passengers embared at Southhampton. I'm replacing the NAs with the same. First, I create a new imputed training dataset.

```{r}
summary(train.raw$Embarked)
train.imp <- train.raw
train.imp$Embarked[is.na(train.imp$Embarked)] <- 'S'
```

**Names, Titles & Age**:

The names have titles embedded in the strings. I can extract these using regex. Master, Miss, Mr and Mrs are the most popular - no surprise there, with lots of other titles.  Here's the distribution of the titles by age. These can be used to impute the missing age values.

```{r, message=FALSE, warning=FALSE}
train.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = train.raw$Name)
train.raw$title <- as.factor(train.raw$title)

train.raw %>%
    na.omit() %>%
    group_by(title) %>%
    dplyr::summarise(Count=n(), Median_Age=round(median(Age),0)) %>%
    arrange(-Median_Age)
```

```{r, message=FALSE, warning=FALSE}
ggplot(train.raw,aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(shape=21,alpha=.6,col='blue')+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')
```

Grouping similar titles together, I've kept a few titles - Officer, Royalty, Mr, Mrs and Miss.

```{r}
train.imp <- train.raw
train.imp$title <- as.character(train.imp$title)
train.imp$title[train.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
train.imp$title[train.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
train.imp$title[train.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
train.imp$title[train.imp$title %in% c('Ms','Mlle')] <- 'Miss'
train.imp$title <- as.factor(train.imp$title)

train.imp %>%
    group_by(title) %>%
    summarise(Median_Age=median(Age,na.rm = T))
```

```{r}
ggplot(train.imp,aes(x=title,y=Age))+
    geom_jitter(color='blue',shape=21,alpha=.7)+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1))+
    labs(caption='Red points are median values')
```

Now for the missing Age values. I'm trying out two strategies to impute age, just for kicks. First, a regression tree using the `rpart` method. 5-repeat 10-fold cross validation across a tuning grid of 20 values of `maxdepth`. RMSE stablizes at a depth of 14, with a value of 12.2.

```{r}
age.predictors <- train.imp %>%
    dplyr::select(-Survived,-Cabin,-Ticket,-Name) %>%
    filter(complete.cases(.))
set.seed(1234)
ctrl <- trainControl(method = "boot",
                     repeats = 5,
                     number = 200
                     )
rpartGrid <- data.frame(maxdepth = seq(4,20,2))
rpartFit <- train(Age~.,
                  data=age.predictors,
                  method='rpart2',
                  trControl = ctrl,
                  tuneGrid = rpartGrid
                  )
rpartFit
plot(rpartFit)
plot(rpartFit$finalModel,margin=0.02)
text(rpartFit$finalModel,cex=0.8)
```

Another way is to run a randomforest with a search over values of `mtry` using 5-repeat 10-fold cross validation. As we can see mtry=4 is the optimal value which results in the lowest RMSE of 11.4; much better than the rpart model.

```{r}
set.seed(1234)
rfGrid <- data.frame(mtry=seq(1,6,1))
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5
                     )
rfFit <- train(Age~.,
                  data=age.predictors,
                  method='rf',
                  trControl = ctrl,
                  tuneGrid = rfGrid)
rfFit
plot(rfFit)
```

I'm going to use the randomForest model. Using the `predict.train()` to predict values of age and plug them back into the imputed data. You can see the blue points which are the imputed values of `Age`. What I noticed is that for all the titles, the imputed Age value seems to be distributed fairly well, except Master. For Master, the three imputed are definitely outliers. I'm going to force these to the median Age.

```{r}
missing.age <- train.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
train.imp %>%
    mutate(AgeMissing = is.na(Age),
           Age = ifelse(AgeMissing,age.predicted,Age)) %>%
    ggplot(aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(aes(y=Age,col=AgeMissing),shape=2)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')
train.imp$Age[is.na(train.imp$Age)] <- age.predicted
train.imp$Age[train.imp$title=='Master' & train.imp$Age > 20] <- median(train.imp$Age[train.imp$title=='Master'],na.rm = T)
```

## Derived Variables


**Child?:** Trying out two engineered variables here - is the passenger a child or not? Using Age=18 as a threshold. And is s/he close enough to be considered a adult by chance? Those between 16 and 18 could be mistaken for not being children. (My way of incorporating a fudge factor in the decision process of ladies & children first.)

```{r}
train.imp$child <- 0
train.imp$child[train.imp$Age<18] <- 1
train.imp$almostadult <- as.numeric(between(train.imp$Age,16,18))
```

**Really young, or really old?:** Really young ones and older folks would get priority perhaps. Creating two categorical binary variables for these conditions.
```{r}
train.imp$Young <- ifelse(train.imp$Age<10,1,0)
train.imp$Seniors <- ifelse(train.imp$Age>60,1,0)
```


**Family related:** Let's also create some variables that talk about family sizes. What's the total family size -- continous variable `TotalFam`. Is the person single, part of a couple or a large family? Three categorical variables for these.

```{r}
train.imp$TotalFam <- train.imp$SibSp + train.imp$Parch + 1
train.imp$LargeParCh <- as.numeric(train.imp$Parch>=3)
train.imp$LargeSibSp <- as.numeric(train.imp$SibSp>=3)
train.imp$Single <- ifelse(train.imp$TotalFam==1,1,0)
train.imp$Couple <- ifelse(train.imp$TotalFam==2,1,0)
train.imp$Family <- ifelse(train.imp$TotalFam>4,1,0)
train.imp$Name <- NULL
```

**Cabin related:** Extracting the cabin alphabet and number from the cabin variable. Since the cabin numbers could be ordered from left to right or top to bottom on the boat, perhaps only the 1st digit is significant. Also, some folks have more than 1 cabin. Wonder if that's important. Since lots of unknowns in the `Cabin` variable, all NA values are replaced by 'U'. Refering to the deck diagram, the topmost decks are A and B, which are closest to the lifeboats. Perhaps that's important too. Here, I create a bunch of categorical variables based off the original `Cabin`, and then remove it from the dataset.

```{r}
train.imp$CabinMissing <- as.numeric(is.na(train.raw$Cabin))

train.imp$CabinCode <- map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
train.imp$CabinCode[is.na(train.imp$CabinCode)] <- 'U'

train.imp$CabinNum <- as.numeric(map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
train.imp$CabinNum <- map_int(train.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
train.imp$CabinNum[is.na(train.imp$CabinNum)] <- 0

train.imp$TopDeck <- ifelse(train.imp$CabinCode %in% c('A','B'),1,0)
train.imp$MidDeck <- ifelse(train.imp$CabinCode %in% c('C','D'),1,0)
train.imp$LowerDeck <- ifelse(train.imp$TopDeck==0 & train.imp$MidDeck ==0 ,1,0)

train.imp$NumberofCabins <- map_int(train.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
train.imp$Cabin <- NULL
```

**Ticket:** Lastly, the `ticket` variable. I'm not sure what to make of it, so I'm keeping it for now, after cleaning it up a bit. A majority (80%) of the rows have unique (one) ticket. 14% rows have a duplicate ticket, perhaps indicating a family. A small number of rows have 3+ duplicates of the tickets.

```{r}
train.imp$Ticket %>% table() %>% as.numeric() %>% table()
```

There seems to be a bit of a pattern here. Tickets starting with 1 are mostly 1st class, those starting with 2 are 2nd class, and 3 - 3rd class. But, I feel it's a very loose association.
```{r}
train.imp %>% group_by(Pclass) %>% dplyr::select(Ticket,Pclass) %>% sample_n(5)
```

What I'm going to do is clean up the columns (remove special characters, spaces etc), then split the `Ticket` column into four: `TicketChar`, `TicketNum`,`TicketNumLength`, `TicketNumStart`.  (Upon running the script a few times, I've decided to get rid of `TicketNum`, but I'm commenting the code for future ref). The `TicketChar` variable as this distribution:

```{r}
train.imp %<>%
    mutate(
        Ticket = str_to_upper(Ticket) %>%
            str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
        TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
        TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
        TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
        TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+')) 
        ) %>%
     mutate(
         TicketChar = map_chr(.x=TicketChar,
                              .f=~str_split(string=.x, pattern = '',simplify = T)[1])
         ) %>%     
    mutate(
        TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
        TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
        TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
    )
train.imp$Ticket <- NULL
train.imp$TicketNum <- NULL
table(train.imp$TicketChar,dnn ='TicketChar')
table(train.imp$TicketNumLen,dnn='TicketNumLen')
table(train.imp$TicketNumStart,dnn='TicketNumStart')
```

## Winzoring Variables

The `fare` variable has one massive outlier. Winzorising this variable using the 95th percentile value as the cutoff.

```{r}
# ggplot(train.imp,aes(x=Fare,fill=Pclass))+geom_histogram()+facet_grid(Pclass~.)
# quantile(train.imp$Fare[train.imp$Pclass=='P1'],probs = c(.1,.25,.5,.75,.95))
# train.imp$Fare[train.imp$Fare>232] <- 232
```

## Final Data Review

The dataset is now prepared for modeling. Here's a quick review of the data so far. 29 variables in total.

```{r}
train.imp %>% glimpse()
```


***

# Modeling

I'm experimenting with a few modeling techniques, mainly [xgboost](http://xgboost.readthedocs.io/en/latest/), [gbm](https://cran.r-project.org/web/packages/gbm/index.html), and penalized models using [glmnet](https://cran.r-project.org/web/packages/glmnet/index.html). I've implemented all these models using [caret](http://topepo.github.io/caret/index.html) which I find an absolutely indispensible toolkit to prep, build, tune and explore numerous models using very few lines of code.

For all models, I'm using a 5-repeat 10-fold cross validation technique on the training dataset. Thus, I have not split the training dataset further into test-train sets, given the small number of observations in the dataset. 

Furthermore, given the 80:20 class-imbalance, I'm also trying out [smote](https://www.jair.org/media/953/live-953-2037-jair.pdf) as an class balancing technique for a few models. 

Tuning parameter searches (aka hypertuning) is performed using the `tuneGrid` parameter in the `train()` call. The best model is selected using the AUC of the ROC. Here are the models and a few intermediate results for each model. At the end, I've compared the performance of all the models together.

## Extreme Gradient Boosting (xgboost - 5repeat-10fold-cv)


```{r, echo=TRUE}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary
                     # sampling = 'smote'
                     )
set.seed(1)
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
xgbFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
xgbFit
plot(xgbFit)
xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel) %>%
    xgb.ggplot.importance()

densityplot(xgbFit,pch='|')
predict(xgbFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)
```

## Extreme Gradient Boosting (xgboost - 5test-train split)
```{r, echo=TRUE}
ctrl <- trainControl(method = 'LGOCV',
                     number = 50,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary
                     # sampling = 'smote'
                     )
set.seed(1)
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
xgbFit.LGOCV <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
xgbFit.LGOCV
plot(xgbFit.LGOCV)
xgb.importance(feature_names = colnames(Dtrain),model = xgbFit.LGOCV$finalModel) %>%
    xgb.ggplot.importance()

densityplot(xgbFit.LGOCV,pch='|')
predict(xgbFit.LGOCV,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)
```

## Extreme Gradient Boosting (xgboost) - SMOTE Sampling
```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
set.seed(1)
xgbsmoteFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
xgbsmoteFit
plot(xgbsmoteFit)
xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbsmoteFit,pch='|')
predict(xgbsmoteFit,type = 'raw') -> train.Class
predict(xgbsmoteFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)
```

## Extreme Gradient Boosting (xgboost) - down Sampling
```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'down'
                     )
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
set.seed(1)
xgbdownFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
xgbdownFit
plot(xgbdownFit)
xgb.importance(feature_names = colnames(Dtrain),model = xgbdownFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbdownFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbdownFit,pch='|')
predict(xgbdownFit,type = 'raw') -> train.Class
predict(xgbdownFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)
```

## Gradient Boosting (gbm)

```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     adaptive = list(min = 5, alpha = 0.05, 
                                     method = "gls", complete = TRUE),
                     search = 'random'
                     )
gbmGrid <- expand.grid(
   n.trees=c(500,700,900,1100),
   interaction.depth=c(1,2,3),
   shrinkage=c(0.1,0.01),
   n.minobsinnode=10
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
set.seed(1)
boostFit <- train(
    x = Dtrain,
    y = boost.train$Survived,
    trControl=ctrl,
    method='gbm',
    tuneGrid=gbmGrid
)
boostFit
plot(boostFit)
xyplot(oobag.improve~1:1100,data=boostFit$finalModel,alpha=.5,xlab = 'n.trees')
plot(varImp(boostFit))
densityplot(boostFit,pch='|')
predict(boostFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)
```

## Random Forest (custom rf)
```{r}

customRF <-
    list(type = "Classification",
    library = "randomForest",
    loop = NULL)

customRF$parameters <- data.frame(
    parameter = c("mtry", "ntree", 'nodesize', 'replace'),
    class = c("numeric", 'numeric', 'numeric', 'logical'),
    label = c("mtry", "ntree", 'nodesize', 'replace')
    )
customRF$grid <- function(x, y, len = NULL, search = "grid") {}

customRF$fit <-
    function(x,
    y,
    wts,
    param,
    lev,
    last,
    weights,
    classProbs,
    ...) {
    randomForest(
    x,
    y,
    mtry = param$mtry,
    ntree = param$ntree,
    replace = param$replace,
    # classwt = param$classwt,
    nodesize = param$nodesize
    )
    }
customRF$predict <-
    function(modelFit,
    newdata,
    preProc = NULL,
    submodels = NULL)
    predict(modelFit, newdata)

customRF$prob <-
    function(modelFit,
    newdata,
    preProc = NULL,
    submodels = NULL)
        predict(modelFit, newdata, type = "prob")
customRF$sort <- function(x)
    x[order(x[, 1]), ]

customRF$levels <- function(x)
    x$classes

customRF
    
    
ctrl <- trainControl(
    method = "repeatedcv",
    repeats = 5,
    verboseIter = T,
    classProbs = TRUE,
    summaryFunction = twoClassSummary,
    # sampling = 'smote'
)
crfGrid <- expand.grid(
    mtry = c(10, 15, 20, 29),
    ntree = c(100, 300, 500),
    nodesize = c(1, 5, 10),
    replace = c(T, F)
    # classwt = c(0.6, 0.8)
)
set.seed(1)
crfFit <- train(
    form = Survived ~ .,
    data = train.imp,
    method = customRF,
    trControl = adapt_ctrl,
    # metric = "Kappa",
    tuneGrid = crfGrid,
    verbose = TRUE,
    classwt = c(0.38, 0.61)
)
crfFit_class <- train(
    form = Survived ~ .,
    data = train.imp,
    method = customRF,
    trControl = adapt_ctrl,
    # metric = "Kappa",
    tuneGrid = crfGrid,
    verbose = TRUE,
    classwt = c(0.38, 0.61)
)
    
```

## Random Forest (rf)
```{r}
adapt_ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     adaptive = list(min = 5, alpha = 0.05, 
                                             method = "gls", complete = TRUE),
                     search = 'random'
                     )

rfGrid <- expand.grid(mtry=c(5,10,15,20,25))
set.seed(1)
rfFit.y <- train(
    form = Survived~.,
    data = train.imp,
    method = 'rf',
    trControl = adapt_ctrl,
    tuneGrid = rfGrid,
    verbose = TRUE,
    ntree = 400
)
rfFit.y
plot(rfFit.y)
plot(rfFit.y$finalModel)
densityplot(rfFit.y,pch='|')
predict(rfFit.y,type = 'prob') -> train.rf.Probs
histogram(~Survived+Dead,train.rf.Probs)
```

## Random Forest (rf) - SMOTE

```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )

rfGrid <- expand.grid(mtry=c(5,10,15,20,25))
set.seed(1)
rfsmoteFit.y <- train(
    form = Survived~.,
    data = train.imp,
    method = 'rf',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = rfGrid,
    verbose = TRUE,
    ntree = 400
)
rfsmoteFit.y
plot(rfsmoteFit.y)
plot(rfsmoteFit.y$finalModel)
densityplot(rfsmoteFit.y,pch='|')
predict(rfsmoteFit.y,type = 'prob') -> train.rfsmoteFit.Probs
histogram(~Survived+Dead,train.rfsmoteFit.Probs)
```

## Elastinet

```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )
glmnetGrid <- expand.grid(.alpha = c(0,.1,.2,.4,.6,.8,1),
                          .lambda = seq(0.01,0.2,length.out = 40))
set.seed(1)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
glmnetFit <- train(
    x = Dtrain,
    y = train.imp$Survived,
    trControl=ctrl,
    method='glmnet',
    tuneGrid=glmnetGrid
)
glmnetFit
plot(glmnetFit,plotType='level')
plot(varImp(glmnetFit))
densityplot(glmnetFit,pch='|')
predict(glmnetFit,type = 'prob') -> train.glmnet.Probs
histogram(~Survived+Dead,train.glmnet.Probs)

```

# Compare models
```{r}
re <-
    resamples(
    x = list(
    xgb = xgbFit,
    xgbsmote = xgbsmoteFit,
    xgbdown = xgbdownFit,
    rf = rfFit.y,
    rfsmote = rfsmoteFit.y,
    gbm = boostFit,
    elastinet=glmnetFit,
    crf = crfFit,
    crfFit_class= crfFit_class
    )
    )
summary(re)
bwplot(re)
summary(diff(re))
```

Calibration curves...
```{r}
simulatedTrain <- data.frame(Class = train.imp$Survived)
simulatedTrain$rf = predict(rfFit.y,type = 'prob')[[1]]
simulatedTrain$rfsmote = predict(rfsmoteFit.y,type = 'prob')[[1]]
simulatedTrain$xgb = predict(xgbFit,type = 'prob')[[1]]
simulatedTrain$xgbsmote = predict(xgbsmoteFit,type = 'prob')[[1]]
simulatedTrain$boost = predict(boostFit,type = 'prob')[[1]]
```
```{r}
calCurve <- calibration(x = Class~rf+rfsmote+xgb+xgbsmote+boost,data = simulatedTrain)
xyplot(calCurve,auto.key=list(columns=3))
```

# Calibrating probabilities

```{r}
boostsigmoidCal <- glm(relevel(Class,ref='Dead')~boost,simulatedTrain,family = 'binomial')
coef(summary(boostsigmoidCal))
simulatedTrain$boostSig = predict(boostsigmoidCal,type = 'response')

xgbsmotesigmoidCal <- glm(relevel(Class,ref='Dead')~xgbsmote,simulatedTrain,family = 'binomial')
coef(summary(xgbsmotesigmoidCal))
simulatedTrain$xgbsmoteSig = predict(xgbsmotesigmoidCal,type = 'response')

calibration(x = Class~boost+boostSig,data = simulatedTrain) %>%
    xyplot(auto.key=list(columns=2))
calibration(x = Class~xgbsmote+xgbsmoteSig,data = simulatedTrain) %>%
    xyplot(auto.key=list(columns=2))
```


# Test Set Evaluation
## Create test set
```{r}
test.imp <- test.raw

#Embarked
test.imp$Embarked[is.na(test.imp$Embarked)]='S'

#Title
test.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = test.raw$Name)
#test.raw$title <- as.factor(test.raw$title)
test.imp$title <- as.character(test.raw$title)
test.imp$title[test.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
test.imp$title[test.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
test.imp$title[test.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
test.imp$title[test.imp$title %in% c('Ms','Mlle')] <- 'Miss'
test.imp$title <- as.factor(test.imp$title)

#Missing age
missing.age <- test.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
test.imp$Age[is.na(test.imp$Age)] <- age.predicted
test.imp$Age[test.imp$title=='Master' & test.imp$Age > 20] <- 4

#Child
test.imp$child <- 0
test.imp$child[test.imp$Age<18] <- 1
test.imp$almostadult <- as.numeric(between(test.imp$Age,16,18))

#Young/old
test.imp$Young <- ifelse(test.imp$Age<10,1,0)
test.imp$Seniors <- ifelse(test.imp$Age>60,1,0)

#Family Related
test.imp$TotalFam <- test.imp$SibSp + test.imp$Parch + 1
test.imp$Name <- NULL
test.imp$LargeParCh <- as.numeric(test.imp$Parch>=3)
test.imp$LargeSibSp <- as.numeric(test.imp$SibSp>=3)
test.imp$Single <- ifelse(test.imp$TotalFam==1,1,0)
test.imp$Couple <- ifelse(test.imp$TotalFam==2,1,0)
test.imp$Family <- ifelse(test.imp$TotalFam>4,1,0)

#Cabin & Deck
test.imp$CabinMissing <- as.numeric(is.na(test.raw$Cabin))
test.imp$CabinCode <- map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
test.imp$CabinCode[is.na(test.imp$CabinCode)] <- 'U'
test.imp$CabinNum <- as.numeric(map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
test.imp$CabinNum <- map_int(test.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
test.imp$CabinNum[is.na(test.imp$CabinNum)] <- 0

test.imp$CabinCode <- factor(
    x = test.imp$CabinCode,
    levels = unique(train.imp$CabinCode)
)

test.imp$TopDeck <- ifelse(test.imp$CabinCode %in% c('A','B'),1,0)
test.imp$MidDeck <- ifelse(test.imp$CabinCode %in% c('C','D'),1,0)
test.imp$LowerDeck <- ifelse(test.imp$TopDeck==0 & test.imp$MidDeck ==0 ,1,0)

test.imp$NumberofCabins <- map_int(test.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
test.imp$Cabin <- NULL


# Ticket
test.imp %<>%
    mutate(
      Ticket = str_to_upper(Ticket) %>%
          str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
      TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
      TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
      TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
      TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+'))
      ) %>%
    mutate(
        TicketChar = map_chr(.x=TicketChar,
                             .f=~str_split(string=.x, pattern = '',simplify = T)[1])
        ) %>%  
    mutate(
      TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
      TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
      TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
    )
test.imp$Ticket <- NULL
test.imp$TicketNum <- NULL

#Fare
test.imp$Fare[is.na(test.imp$Fare)] <- 14.4542
# test.imp$Fare[test.imp$Fare>232] <- 232

```
## Predict test results
```{r}
dumV <- dummyVars(formula = ~.,data = test.imp)
Dtest <- predict(dumV,test.imp)

boostPred <- ifelse(predict(boostFit$finalModel,newdata = Dtest,n.trees = boostFit$bestTune$n.trees,type = 'response')>0.5,1,2)
xgbPred <- predict(object = xgbFit, newdata = Dtest)
xgbsmotePred <- predict(object = xgbsmoteFit, newdata = Dtest)
rfPred <- predict(object = rfFit.y, newdata = test.imp)
rfsmotePred <- predict(object = rfsmoteFit.y, newdata = test.imp)

boostProb <- data.frame(boost=predict(boostFit$finalModel,newdata = Dtest,n.trees = boostFit$bestTune$n.trees,type = 'response'))
boostSigPred <- predict(object = boostsigmoidCal,newdata = boostProb,type = 'response')
boostSigPred <- ifelse(boostSigPred>=0.5,1,2)

xgbsmotesigmoidCal
xgbProb <- data.frame(xgbsmote=predict(object = xgbsmoteFit, newdata = Dtest,type = 'prob')[[1]])
xgbsmoteSigPred <- predict(object = xgbsmotesigmoidCal,newdata = xgbProb,type = 'response')
xgbsmoteSigPred <- ifelse(xgbsmoteSigPred>0.5,1,2)
```


## Write results
```{r}
PID <-
    readData(Titanic.path,
    test.data.file,
    test.column.types,
    missing.types)
PID <- PID$PassengerId
```
```{r}
for(m in ls(pattern = 'Pred')) {
    write.csv(
    x = data.frame(
    PassengerId = PID,
    Survived = as.numeric(eval(parse(text = m))) * -1 + 2
    ),
    file = paste0(m,'.csv'),
    row.names = F
    )
}
```

# Conclusions



# Misc - delete later...
```{r}
d <- data.frame(boostPred,xgbPred,rfPred,xgbsmotePred,rfsmotePred,boostSigPred)
map_df(d,~as.numeric(.x)*-1+2) -> d
d %>% mutate(Avg=rowMeans(.)) -> d
d %>% rownames_to_column('rowid') %>% reshape2::melt(id.vars='rowid') %>% 
    ggplot(aes(x=rowid, y=value))+
    geom_point(aes(col=variable))
```

```{r}
train.imp %>% 
    bind_cols(simulatedTrain) %>%
    mutate(Young=as.factor(Young)) %>% 
    group_by(Young) %>%
    summarize(
        A=mean(Age,na.rm = T),
        S=sd(Age)
    )
```